Download Rewiring the retinal ganglion cell gene regulatory

RESEARCH ARTICLE 3379
Development 135, 3379-3388 (2008) doi:10.1242/dev.024612
Rewiring the retinal ganglion cell gene regulatory network:
Neurod1 promotes retinal ganglion cell fate in the absence
of Math5
Chai-An Mao1, Steven W. Wang2, Ping Pan1 and William H. Klein1,3,*
Retinal progenitor cells (RPCs) express basic helix-loop-helix (bHLH) factors in a strikingly mosaic spatiotemporal pattern, which is
thought to contribute to the establishment of individual retinal cell identity. Here, we ask whether this tightly regulated pattern is
essential for the orderly differentiation of the early retinal cell types and whether different bHLH genes have distinct functions that
are adapted for each RPC. To address these issues, we replaced one bHLH gene with another. Math5 is a bHLH gene that is essential
for establishing retinal ganglion cell (RGC) fate. We analyzed the retinas of mice in which Math5 was replaced with Neurod1 or
Math3, bHLH genes that are expressed in another RPC and are required to establish amacrine cell fate. In the absence of Math5,
Math5Neurod1-KI was able to specify RGCs, activate RGC genes and restore the optic nerve, although not as effectively as Math5. By
contrast, Math5Math3-KI was much less effective than Math5Neurod1-KI in replacing Math5. In addition, expression of Neurod1 and
Math3 from the Math5Neurod1-KI/Math3-KI allele did not result in enhanced amacrine cell production. These results were unexpected
because they indicated that bHLH genes, which are currently thought to have evolved highly specialized functions, are nonetheless
able to adjust their functions by interpreting the local positional information that is programmed into the RPC lineages. We
conclude that, although Neurod1 and Math3 have evolved specialized functions for establishing amacrine cell fate, they are
nevertheless capable of alternative functions when expressed in foreign environments.
INTRODUCTION
In vertebrates, postmitotic retinal neurons are generated from a pool
of multipotent retinal progenitor cells (RPCs) in a temporal order:
retinal ganglion cells (RGCs) are produced first, followed
immediately by amacrine cells, horizontal cells and cone
photoreceptor cells in highly overlapping waves of cell
differentiation. Subsequent to the formation of these early neuronal
cell types, the later cell types form: bipolar cells, rod photoreceptor
cells and, lastly, Müller glial cells. Distinct RPCs integrate their
intrinsic programs with local environmental signals to define their
competence states and to commit to individual retinal cell fates
(Livesey and Cepko, 2001). Many transcription factors have been
implicated in setting up the competence states of RPCs (Hatakeyama
and Kageyama, 2004; Ohsawa and Kageyama, 2008). In the mouse,
Math5 (Atoh7 – Mouse Genome Informatics), an ortholog of the
Drosophila bHLH proneural gene Atonal, is expressed in a
subpopulation of RPCs and is essential for establishing RGC
competence (Brown et al., 1998; Brown et al., 2001; Wang et al.,
2001; Yang et al., 2003; Mu et al., 2005).
A remarkable feature of retinal development is that RPCs are
capable of simultaneously producing multiple cell types,
suggesting the presence of subpopulations RPCs with each
possessing a distinct genetic makeup. Unfortunately, these
genetically distinct RPC subpopulations have not been clearly
1
Department of Biochemistry and Molecular Biology, The University of Texas M. D.
Anderson Cancer Center, Houston, TX 77030, USA. 2Department of Ophthalmology
and Visual Science, The University of Texas Houston Medical School, Houston,
TX 77030, USA. 3Training Program in Genes and Development, The University of
Texas Graduate School of Biomedical Sciences at Houston, Houston, TX 77030, USA.
*Author for correspondence (e-mail: [email protected])
Accepted 26 August 2008
defined by conventional cell lineage tracing experiments
(reviewed by Mu and Klein, 2004; Mu and Klein, 2008). The
bHLH genes Math5, Mash1 (Ascl1 – Mouse Genome Informatics),
Math3 (Neurod4 – Mouse Genome Informatics) and Neurod1 are
expressed in the developing retina at overlapping times but in
largely distinct, interspersed RPC subpopulations (Vetter and
Brown, 2001; Akagi et al., 2004; Hatakeyama and Kageyama,
2004; Le et al., 2006; Ohsawa and Kageyama, 2008; Trimarchi et
al., 2008). The bHLH factors collaborate with homeobox factors
to specify particular retinal cell fates at the expense of others
(Wang and Harris, 2005; Cayouette et al., 2006; Ohsawa and
Kageyama, 2008). However, in early developing retina, many key
homeobox genes, such as Pax6 and Six3, are expressed in virtually
all RPCs (Lagutin et al., 2001; Bäumer et al., 2003) and have
multiple functions in specifying distinct cell fates (Ohsawa and
Kageyama, 2008). It is therefore likely that the mosaic expression
pattern of bHLH genes more accurately mirrors the state of
competency within each individual RPC for the early retinal cell
types (Cayouette et al., 2006). This concept implies that a unique
bHLH gene expression pattern regulates the competence state of
each RPC fate for the early differentiating cell types, with more
widely expressed homeobox factors acting in conjunction with the
bHLH factors. Thus, replacing one bHLH gene with another might
be expected to redirect the RPC to assume the competence state
defined by the replacing bHLH gene. However, it is also possible
that this type of replacement would not be tolerated because the
replacing bHLH gene, which might have evolved specialized
functions in the retina, would be incapable of integrating into the
intrinsic program of a foreign RPC. A final possibility is that
replacing one bHLH gene with another would restore the original
RPC lineage. If this were the case, it would suggest that retinal
bHLH genes might not be highly specialized and therefore are
susceptible to the intracellular environment of the foreign RPC.
DEVELOPMENT
KEY WORDS: Retinal ganglion cells, Retinal progenitor cells, bHLH genes, Math5 (Atoh7), Neurod1, Math3 (Neurod4)
To determine which of these possibilities actually occurs, we
replaced Math5 with either Neurod1 or Math3, which are required
together for establishing amacrine cell fate, and are capable of
producing excess numbers of amacrine cells when each of them is
ectopically co-expressed with Pax6 or Six3 (Inoue et al., 2002).
Here, we demonstrate that Neurod1 can partially rescue the
functions of Math5 in RGC production. By contrast, Math3 could
only modestly rescue Math5 mutant defects by activating some
RGC genes. In addition, Neurod1 and Math3 co-expression at the
Math5 locus does not lead to the overproduction of amacrine cells.
Our results demonstrate that although Neurod1 and Math3 have
evolved specialized functions, they are nevertheless capable of
alternative functions when expressed in a foreign environment.
These results suggest that RPC heterogeneity is largely programmed
by intrinsic mechanisms that are not solely dependent on a specific
bHLH gene.
MATERIALS AND METHODS
Gene targeting and animal breeding
To construct the targeting vector, genomic DNA from G4 ES cells (George
et al., 2007) was used for the polymerase chain reaction (PCR) amplification
of 5 kb fragments representing the left and right homologous replacement
arms from the Math5 locus (Fig. 1B). Complete coding regions for Neurod1
and Math3 are located in single exons, and their sequences were amplified
using G4 ES cell genomic DNA. These fragments were sequentially
subcloned into a targeting vector (Mao et al., 2008). The resulting constructs
were linearized and electroporated into G4 ES cells, after which G418resistent ES cells were selected to identify homologous recombination
events. A 5⬘ probe from outside the homologous recombination region was
used to detect 20.5 kb wild-type and 15.6 kb knock-in fragments produced
by BamHI digestion of ES cell DNA (Fig. 1B,C). Two targeted ES cell lines
for each construct were expanded and injected into B6(GC)-Tyrc-2J/J
blastocysts, and the injected blastocysts were transferred into the uteri of
pseudopregnant C57/BL/6J female mice. Chimeric males resulting from the
injected blastocysts were bred to B6(GC)-Tyrc-2J/J females (Jackson
Laboratory) to generate Math5Neurod1-KI/+ and Math5Math3-KI/+ heterozygotes.
The two lines were subsequently bred to Math5lacZ-KI/+ mice (Wang et al.,
2001) to generate Math5Neurod1-KI/lacZ-KI and Math5Math3-KI/lacZ-KI lines.
Math5Neurod1-KI, Math5Math3-KI, Math5lacZ-KI and Math5 wild-type alleles
were distinguished using a Southern blot genome hybridization probe and
EcoRV-digested genomic DNA (see Fig. 1D). Embryos were designated as
embryonic day 0.5 (E0.5) at noon on the day in which vaginal plugs were
observed.
All animal procedures in this study followed the US Public Health Service
Policy on Humane Care and Use of Laboratory Animals and were approved
by the Institutional Animal Care and Use Committee at The University of
Texas M. D. Anderson Cancer Center.
Histology, in situ hybridization and immunohistochemical analysis
Embryos and eyes dissected from embryos or adults were fixed, paraffinembedded and sectioned into 7 μm or 12 μm slices for immunohistochemical
analysis or in situ hybridization, respectively, as described by Mao et al. (Mao
et al., 2008). After de-waxing and rehydration, the sections were stained with
Hematoxylin and Eosin before further analysis. In situ hybridization was
performed as described by Mu et al. (Mu et al., 2004).
For immunohistochemical analysis, sections were placed in a
microwave oven at 600 W in 10 mM sodium citrate for 15 minutes to
expose the antigen epitopes. Microwave-treated sections were then
incubated with the primary antibodies listed below. For indirect
immunofluorescence, a tyromide signal amplification kit (TSA biotin
system, PerkinElmer) was used for Neurod1 and Eomes to optimize
signal intensity. For double immunofluorescence, Alexa-conjugated
secondary antibodies (Invitrogen) were used. The primary antibodies
were anti-Brn3b/Pou4f2 (Santa Cruz Biotech, 1:200 dilution), anti-Chx10
(Exalpha, 1:1000 dilution), anti-GSK3β (Cell Signaling, 1:400 dilution),
anti-NFL (Invitrogen, 1:250 dilution), anti-NF160 (DSHB, 1:1000
dilution), anti-Isl1 (DSHB, 1:250 dilution), anti-melanopsin (provided by
Development 135 (20)
Satchidananda Panda, Salk Institute), anti-SMI32 (Covance, 1:1000
dilution), anti-Tbr2/Eomes (Chemicon, 1:1000 dilution), anti-Sox9
(Chemicon, 1:200 dilution), anti-TUJ1 (Covance, 1:500 dilution), antiChAT (Chemicon, 1:100 dilution), anti-opsin(R/G) (Chemicon, 1/200
dilution), anti-calrectinin (Chemicon, 1:2500 dilution), anti-calbindin
(Swant, 1:5000 dilution), anti-Pax6 (DSHB, 1:200 dilution) and anti-p57
(Santa Cruz, 1:40 dilution). Horseradish peroxidase-conjugated
secondary antibody for tyromide signal amplification was obtained from
Jackson ImmunoResearch. To detect RGC axons, anti-NFL antibody was
used to stain flat-mounted adult retinas (Mao et al., 2008). The number of
axonal bundles and the axonal density within each bundle were analyzed
with the same settings using SimplePCI software (Compix, Sewickley,
PA) to automatically select regions of interest from the peripheral retinal
flat-mount images. Flat-mount immunostaining was also used to monitor
the distribution of melanopsin and SMI-32 positive RGCs. For
quantifying RGC specification during embryonic stages, Pou4f2-positive
cells were used to estimate RGC number. Three retinal sections (three
sections apart) collected from littermates of different genotypes were
stained with anti-Brn3b/Pou4f2 antibody, and the number of Pou4f2positive cells on each section was counted on an Olympus Fluoview 1000
confocal microscope.
Quantitative reverse transcriptase-PCR analysis
Total RNA were collected from two E13.5 retinas using TRIZOL reagent
(Invitrogen, CA). RNAs were reversed transcribed using Superscript FirstStrand Synthesis System for reverse transcriptase (RT)-PCR (Invitrogen)
following the manufacturer’s instruction. One twentieth of the total
cDNAs was amplified for quantitative (q)PCR using SYBR green PCR
master mix (Applied Biosystems, CA). The relative expression levels were
normalized to that of GAPDH and calculated using the comparative Ct
method (7500 Fast Real-time PCR systems SDS software, Applied
Biosystems). DNA sequences of PCR primers indicated were: Math5
5⬘UTR forward, 5⬘-TCCGTCTGTGTCTTATTCACTC-3⬘; Math5
reverse, 5⬘-TTTGCAGGCCGACTTCATCCTC-3⬘; Math3 reverse, 5⬘ATATACATTTTTGCCATGGCCGC-3⬘; GAPDH forward, 5⬘-AGGTCGGTGTGAACGGATTTG-3⬘; GAPDH reverse, 5⬘-TGTAGACCATGTAGTTGAGGTCA-3⬘.
RESULTS
Expression of Neurod1 and Math3 in
Math5Neurod1-KI and Math5Math3-KI embryonic retinas
The bHLH sequences from Math5, Neurod1 and Math3 are similar
but not identical, clustering together with the other mouse bHLH
genes (Fig. 1A) (Ledent et al., 2002). Outside of the bHLH
domain, significant differences in Math5, Neurod1 and Math3
sequences indicate that these bHLH genes have undergone extensive
divergence since their emergence from a common ancestral gene.
A sequence tree shows that Math5 is rooted in a clade distinct from
that of Neurod1 and Math3, and that Neurod1 and Math3 are
closely related to each other in their bHLH domains (Fig. 1A). To
determine the effects of replacing Math5 with Neurod1 or Math3,
we used targeting constructs to target embryonic stem (ES) cells in
which we removed the entire Math5 sequence and replaced it with
either a Neurod1 or a Math3 sequence (Fig. 1B). Germline mice
containing Math5Neurod1-KI or Math5Math3-KI alleles were bred to
Math5lacZ-KI/lacZ-KI mice (Math5-null mice) and to each other to
generate mice with the following genotypes: Math5Neurod1-KI/+
(Fig. 1C), Math5Neurod1-KI/lacZ-KI, Math5Neurod1-KI/Neurod1-KI (Fig.
1D), Math5Math3-KI/+, Math5Math3-KI/lacZ-KI, Math5Math3-KI/Math3-KI and
Math5Neurod1-KI/Math3-KI.
We first determined whether Math5Neurod1-KI and Math5Math3-KI
alleles were expressed in a pattern mimicking that of Math5. At
E12.5, we detected low expression levels of Neurod1 protein in
the retinas of wild-type mice (Fig. 1E). By contrast, high levels of
Neurod1 expression were observed in Math5Neurod1-KI/Math3-KI
DEVELOPMENT
3380 RESEARCH ARTICLE
bHLH genes and retinal ganglion cell fate
RESEARCH ARTICLE 3381
Fig. 1. Replacing endogenous
Math5 with Neurod1 or Math3.
(A) Sequence relationships among
bHLH domains of representative
proneural bHLH genes [method
described by Ledent et al. (Ledent
et al., 2002)]. Mash1 was chosen
as the outgroup. Branch lengths
are proportional to the distance
between the sequences.
(B) Genome structure for Math5,
the targeting construct and the
predicted structure of the targeted
Neurod1 and Math3 knock-in
alleles. The single Math5 exon is
depicted as a black box. The black
bars underneath indicate the DNA
fragments amplified from genomic
DNA for the targeting construct.
Red boxes depict FRT
recombination sites. Blue boxes
indicate SV40-pA sequence. Black
arrows indicated the PCR primers
used to amplify Math5 and
Math5Math3-KI for qRT-PCR analysis.
The primer sequences are
described in the Materials and
methods. (C) Representative
Southern blot analysis using the 5⬘
probe to distinguish Math5 wildtype and Math5Neurod1-KI alleles
from genomic DNA of targeted ES
cells. O indicates a targeted ES cell.
(D) Representative Southern blot
analysis using the Southern probe
depicted in B to distinguish Math5
wild-type, Math5Neurod1-KI and
Math5lacZ-KI alleles from tail
genomic DNA of littermates
resulting from a
Math5Neurod1-KI/lacZ-KI ⫻ Math5lacZKI/+
cross. (E-H) Misexpression of
Neurod1 and Math3 from the
Math5 locus. Retinal sections from E12.5 wild-type (E,G) and Math5Neurod1-KI/Math3-KI (F,H) embryos were immunostained with anti-Neurod1
antibody (E,F) or labeled with a Math3 antisense probe by in situ hybridization (G,H). The images in G and H have been enhanced using
Photoshop. (I) qRT-PCR analysis of Math5 and Math5Math3-KI alleles. Gene expression levels were normalized to the expression of endogenous
GAPDH transcripts. Scale bar: 100 μm.
similar levels in Math5Math3-KI/+ retinas, and that Math5Math3-KI
in Math5Math3-KI/Math3-KI retinas was expressed at levels
corresponding to those of Math5 in wild-type retinas.
Partial restoration of RGC axons, optic nerves, and
RGC subtypes with Math5Neurod1-KI and Math5Math3-KI
alleles
Math5-null mice have severe optic nerve hypoplasia and in extreme
cases lack optic nerves entirely (Fig. 2A) (Brown et al., 2001;
Wang et al., 2001). Eyes from adult Math5Neurod1-KI/lacZ-KI and
Math5Neurod1-KI/Math3-KI mice 4-5 weeks old (P30) were often found
attached to well-developed optic nerves with diameters that were
50-60% those of wild-type mice (Fig. 2A,B). Histological sections
from Math5Neurod1-KI/lacZ-KI and Math5Neurod1-KI/Math3-KI mice also
revealed substantial restoration of optic nerves (Fig. 2A1,A2,B).
Immunostaining retinas with a Pou4f2/Brn3b antibody, which detects
RGCs, showed that 30-40% of the cells in the ganglion cell layer
DEVELOPMENT
retinas at E12.5 (Fig. 1F). Similar to Neurod1 protein expression,
Math3 transcript expression, although weak, was readily
detectable near the ventricular region in E12.5 wild-type retinas,
and in a pattern similar to that of Neurod1 expression in
Math5Neurod1-KI/Math3-KI retinas at the same developmental time
(Fig. 1G,H). Between E12.5 and E15.5, the expression of
Neurod1 and Math3 from the Math5Neurod1-KI and Math5Math3-KI
alleles closely resembled endogenous Math5 expression,
indicating that ectopic Neurod1 and Math3 expression was under
the control of the Math5 regulatory region. Furthermore, Neurod1
expression did not differ between retinas with Math5Neurod1-KI/+ or
Math5Neurod1-KI/Neurod1-KI genotypes, indicating the accurate
replacement of Math5 by Neurod1 (data not shown). To determine
whether the knock-in Math3 allele expressed transcripts at the
same level as the wild-type Math5 allele, we compared the
expression levels of Math5 and Math5Math3-KI alleles at E13.5. Fig.
1I shows that Math5 and Math5Math3-KI alleles were expressed at
3382 RESEARCH ARTICLE
Development 135 (20)
Fig. 2. Neurod1 and Math3 partially restore the optic nerve in Math5-null mice. (A) Dissected eyes from P30 mice. From left to right:
Math5lacZ-KI/+, Math5Neurod1-KI/lacZ-KI and Math5lacZ-KI/lacZ-KI. (A1,A2) Cross-sections of the optic nerves from Math5lacZ-KI/+ and Math5Neurod1-KI/lacZ-KI
dissected eyes shown in A were stained with Cresyl Fast Violet. (B) Histological sections of P30 eyes from wild-type and Math5Neurod1-KI/Math3-KI
littermates. Arrowheads indicate the optic nerves. The double arrowhead indicates a rosette structure sometimes seen in Math5Neurod1-KI/Math3-KI
retinas (inset). (B1,B2) Pou4f2 expression in wild-type and Math5Neurod1-KI/Math3-KI retinal sections. Representative lateral regions were used for
comparison. ONL, outer nuclear layer; INL, inner nuclear layer; GCL, ganglion cell layer. Scale bars: 50 μm in A1; 200 μm in B.
wild-type and Math5Neurod1-KI/Math3-KI retinas (Fig. 3E-H).
Additionally, the dendrites of melanopsin-expressing RGCs always
arborize to layer 1 of the IPL (insets in Fig. 3E,F; n>40). These data
suggest that Neurod1 can replace Math5 to specify different RGC
subtypes evenly across retina, and these RGC subtypes differentiate
normally with proper dendritic arborization.
Expression of RGC genes in embryonic retinas of
Math5Neurod1-KI and Math5Math3-KI mice
The earliest sign of RGC differentiation begins at E12.5, when the
expression of Pou4f2 and Isl1 is first apparent (Gan et al., 1999;
Elshatory et al., 2007; Mu et al., 2008). These genes encode POU
domain and LIM domain transcription factors, respectively, and
both are required for RGC differentiation (Gan et al., 1999; Mu et
al., 2008; Pan et al., 2008). We used anti-Pou4f2/Brn3b and antiIsl1 antibodies to determine the expression pattern of these
proteins in E13.5 retinas. In Math5lacZ-KI/+ (wild-type) retinas,
Pou4f2 and Isl1 were co-expressed in differentiating RGCs, as we
and others have shown previously (Fig. 4A,A1,A2) (Rachel et al.,
2002; Mu et al., 2008). Expression was largely absent in
Math5lacZ-KI/lacZ-KI retinas (Fig. 4B,B1,B2). We found that retinas
expressing Math5Neurod1-KI allele in the absence of Math5
significantly restored the expression of Pou4f2 and Isl1 (Fig. 4CC2,D-D2), whereas in Math5Math3-KI/lacZ-KI retinas, the expression
of these early RGC markers, although detectable, was appreciably
lower (Table 1). We had shown previously that expression of the
neurofilament protein NF160 is strongly dependent on the
presence of Pou4f2 (Mu et al., 2004). Retinas expressing the
Math5Neurod1-KI allele in the absence of Math5 had significantly
higher expression of NF160 (Fig. 4A3-D3). The expression of
Pou4f2, Isl1 and NF160 is indicative of RGC differentiation, and,
therefore, of the number of RGCs present in the retinas of the
Math5Neurod1-KI and Math5Math3-KI mice. According to this
criterion, we estimated that the numbers of RGCs present in the
Math5Neurod1-KI/LacZ-KI and Math5Math3-KI/LacZ-KI retinas were ~40%
(173/434) and 10% (40/413), respectively, the number of RGCs
in wild-type retinas (Table 1). Furthermore, the expression of
Pou4f2 in Math5Neurod1-KI/lacZ-KI and Math5Math3-KI/lacZ-KI retinas
was significantly lower than that in wild-type controls at E12.5,
DEVELOPMENT
(GCL) of Math5Neurod1-KI/Math3-KI retinas expressed Pou4f2 compared
with wild-type retinas (Fig. 2B1,B2). By contrast, we never observed
optic nerves in the eyes of Math5Math3-KI/lacZ-KI mice, despite the close
relationship between the Neurod1 and Math3 genes. These results
indicated that Neurod1, but not Math3, was capable of partially
restoring optic nerves in Math5-deficient mice.
The presence of optic nerves in Math5Neurod1-KI/Math3-KI and
Math5Neurod1-KI/lacZ-KI mice suggested that RGC axons were forming
and extending into the optic disk. Immunostaining of flat-mount
retinas from P30-P40 mice with an anti-neurofilament light
chain (NFL) antibody revealed that one-third (4/12) of
Math5Neurod1-KI/Math3-KI and two-thirds (4/6) of Math5Neurod1-KI/lacZ-KI
retinas had significantly greater numbers and higher density of RGC
axon bundles compared with Math5-null retinas (Fig. 3A-D).
Although some axons appeared misoriented, the majority of axons
were well bundled and oriented towards the optic disk (Fig. 3C,D).
The lack of a complete rescue of the optic nerve in the knock-in
retinas might be explained by enhanced apoptosis; a significant
increase in cell death was detected in Math5Neurod1-KI/lacZ-KI and
Math5Neurod1-KI/Math3-KI retinas from E11.5 to E16.5 compared with
wild-type controls (data not shown). No differences in RGC axon
number or density were detected when Math5Neurod1-KI/+ and
Math5Math3-KI/+ retinas were compared with wild-type controls (data
not shown). The results demonstrated that Neurod1 could partially
replace Math5 in restoring RGC axons and that Math3 alone could
not replace Math5.
In the mouse retina, RGCs have been categorized into ~12-14
subtypes by morphological criteria (Sun et al., 2002; Coombs et al.,
2006). Across the GCL, RGC subtypes display regular spacing
between cell bodies, and arborize within precise strata of the inner
plexiform layer (IPL). To determine whether RGC subtypes formed
in Math5Neurod1-KI/Math3-KI retinas, we selected two previously
characterized RGC subtype markers, melanopsin and SMI-32 (Lin
et al., 2004), and performed immunostaining with flat-mount retinas.
Melanopsin-expressing RGCs have small somata (~20 μm) and their
dendrites run through the IPL, terminating at layer 1 immediately
underneath the inner nuclear layer (INL). SMI-32 is expressed in
RGCs with large somata (~30 μm). Melanopsin- and SMI-32expressing RGCs both displayed a nonrandom mosaic pattern across
bHLH genes and retinal ganglion cell fate
RESEARCH ARTICLE 3383
Fig. 3. Neurod1 partially restores RGC axons and RGC subtypes in
the absence of Math5. (A-D) Immunostaining of flat-mounted retinas
from P30 mice with anti-NFL antibody to reveal RGC axons. Genotypes
are indicated on the lower left of each panel. Insets in C and D
highlight the misoriented axons. OD, optic disk. (E,F) Representative
images of immunostaining of flat-mount retinas from P30 mice using
anti-melanopsin antibody to reveal the mosaic distribution pattern of
melanopsin-positive RGCs. The insets in E and F show representative
dendritic arborization of melanopsin-positive RGCs in layer 1 of the IPL
(arrowheads). Nuclei of the three nuclear layers are stained with DAPI
(blue). Scale bar: 50 μm. (G,H) Immunostaining of flat-mount retinas
with anti-SMI32 antibody to show the mosaic distribution of the
SMI32-positive RGC subtype.
but recovered to higher levels at E14.5 (Table 1), suggesting a
delayed RGC differentiation in the Math5Neurod1-KI/LacZ-KI and
Math5Math3-KI/LacZ-KI retinas.
We recently identified the T-box-containing transcription factor,
eomesodermin (Eomes), as an essential factor for RGC
differentiation and optic nerve formation (Mao et al., 2008). Eomes
is a direct target gene of Pou4f2, and its expression would therefore
be indicative of a functional Pou4f2 protein. At E14.5, Eomes
protein is co-expressed with Pou4f2 in the innermost RGCs in wildtype retinas (Fig. 5A1-A3) (Mao et al., 2008). At this same time,
Eomes is also weakly expressed in non-RGCs in the RPC
proliferation layer; expression of Eomes in the innermost layer is
absent in Math5- and Pou4f2-null retinas (Mao et al., 2008).
The RGC gene regulatory network is restored in
Math5Neurod1-KI/Math3-KI retinas
The partial restoration of RGC axons and optic nerves in adult
retinas and the expression of the early RGC markers Pou4f2, Isl1,
NF160 and Eomes in Math5Neurod1-KI/Math3-KI retinas indicated that,
together, Neurod1 and Math3 could replace Math5 to activate the
entire RGC gene regulatory network (Mu et al., 2004; Mu et al.,
2005; Mu et al., 2008; Mu and Klein, 2008). We therefore
determined the expression levels of a number of RGC-expressed
genes in wild-type and Math5Neurod1-KI/Math3-KI E14.5 retinas that
had various roles in RGC integrity and physiology, transcriptional
regulation and extracellular signal transduction. The selected
genes included those whose expression was dependent on the
presence of Math5 and Pou4f2 (Persyn, Gap43 and Shh) (Mu et
al., 2004), those whose expression was dependent on Math5 but
not Pou4f2 (Myt1, Stmn2 and TuJ1) (Brown et al., 2001; Mu et al.,
2005), and two genes whose dependence on Math5 and Pou4f2 has
not been determined [GDF11 (Kim et al., 2005), Gsk3β (Tokuoka
et al., 2002 ) (see Table S1 in the supplementary material for
details)].
Fig. 6 shows that all of the genes were expressed in RGCs of
Math5Neurod1-KI/Math3-KI retinas in a similar pattern but at lower
levels than in wild-type controls (compare A1-H1 with A2-H2).
These results strongly suggest that the RGC gene regulatory
network was activated in its entirety in Math5Neurod1-KI/Math3-KI
retinas.
Because Neurod1 and Math3 are required together for amacrine
cell development (Inoue et al., 2002), we determined whether
increased numbers of amacrine cells were present in
Math5Neurod1-KI/Math3-KI retinas. Staining with antibodies against
markers for amacrine cells, including ChAT, p57Kip2, calrectinin
and calbindin, revealed no differences in the numbers of cells
between wild-type and Math5Neurod1-KI/Math3-KI retinas (see Fig. S1
in the supplementary material). However, we detected 20% more
opsin-positive photoreceptors in Math5Neurod1-KI/Math3-KI retinas
than in wild-type retinas (see Fig. S1 in the supplementary
material). The significance of this modest increase was unclear,
DEVELOPMENT
Substantially more Eomes-expressing cells were observed in
Math5Neurod1-KI/Math3-KI E14.5 retinas than in Math5-null retinas (Fig.
5B1). Similarly, more Eomes-expressing cells could be found in
Math5Neurod1-KI/lacZ-KI retinas than in Math5-null retinas (Fig. 5C1).
However, we did not detect Eomes-expressing cells in
Math5Math3-KI/lacZ-KI retina (Fig. 5D1). These results suggest that
Neurod1 alone can rescue Eomes expression in RGCs, but Math3
lacks this activity. By contrast, both Neurod1 and Math3 contribute
to the restoration of Pou4f2 expression observed in the absence of
Math5, and that Neurod1 has stronger restoration capabilities than
Math3 (Fig. 5A2,B2,C2,D2; Table 1).
At E13.5 and E14.5, the numbers of Pou4f2-positive cells in the
Math5Neurod1-KI/lacZ-KI (n>20) or the Math5Math3-KI/lacZ-KI (n>20)
retinas were always much greater than in the Math5lacZ-KI/lacZ-KI
retinas. However, we noticed that many Pou4f2-expressing cells in
these knock-in retinas were abnormally positioned when compared
with wild-type controls, residing in the upper-most region of the
RPC layer (compare Fig. 5A2 with Fig. 5C2,D2). This suggested
that RGCs expressing Neurod1 or Math3 in the absence of Math5
were unable to properly migrate to the GCL. However, in
Math5Neurod1-KI/Math3-KI retinas, this abnormality was partially
corrected (Fig. 5B2). This result suggested that, together, Neurod1
and Math3 were slightly more effective in replacing the functions of
Math5 than was Neurod1 alone.
3384 RESEARCH ARTICLE
Development 135 (20)
Fig. 4. Neurod1 activates early markers of RGC differentiation in the absence of Math5. (A-D3) Immunostaining of retinas from E13.5
embryos with anti-Pou4f2/Brn3b (A-D), anti-Isl1 (A1-D1), merged Pou4f2-Isl1 images (A2-D2) and anti-NF160 (A3-D3). Insets show higher
magnification of indicated areas. (A-A3) Math5lacZ-KI/+, (B-B3) Math5lacZ-KI/lacZ-KI, (C-C3) Math5Neurod1-KI/Math3-KI and (D-D3) Math5Neurod1-KI/lacZ-KI. Scale
bar: 100 μm.
but it might reflect a re-direction of Math5Neurod1-KI/Math3-KIexpressing RPCs to a photoreceptor cell fate. This might occur as
a result of restored Ngn2 expression in Math5Neurod1-KI/Math3-KI
RPCs, as happens in Math5-null RPCs (Brown et al., 2001; Le et
al., 2006). Math3, together with Mash1, is essential for bipolar
cell formation (Tomita et al., 2000). We therefore determined
whether increased numbers of bipolar cells could be detected in
Math5Neurod1-KI/Math3-KI retinas. Fig. S1 shows that there is
no difference in Chx10-positive cells in wild-type and
Math5Neurod1-KI/Math3-KI retinas. The number of Sox9-positive
Müller glial cells also did not change. Math5Math3-KI/LacZ-KI retina
displayed histological phenotypes reminiscent of Math5-null
retina, in which the numbers of all cell types normally found in
the INL are reduced owing to the reduced thickness of the INL,
but the ratio of each cell type was not significantly different from
wild-type controls (Moshiri et al., 2008). We found that the
proportion of amacrine, bipolar, horizontal and Müller cell types
in the INL of Math5Math3-KI/LacZ-KI retina was similar to that of
Math5LacZ-KI/+ retinas (see Table S2 in the supplementary
material). These data suggest that Math3 alone does not
significantly influence cell fate determination when expressed at
the Math5 locus.
Math5Neurod1-KI/+ ⫻ Math5Math3-KI/+
Wild type
Math5Neurod1-KI/Math3-KI
Math5Neurod1-KI/LacZ-KI ⫻ Math5LacZ-KI/+
LacZ-KI/+
Math5
Math5LacZ-KI/LacZ-KI
Math5Neurod1-KI/LacZ-KI
Math5Math3-KI/LacZ-KI ⫻ Math5LacZ-KI/+
LacZ-KI/+
Math5
Math5LacZ-KI/LacZ-KI
Math5Math3-KI/LacZ-KI
E12.5
E13.5
E14.5
181.3±3.8
19.3±1.8
292.7±12.2
109±6.7
829±18.7
532.7±14.2
E12.5
E13.5
E14.5
189.7±3.6
2.3±0.4
29.3±3.8
433.7±14.4
8.7±0.4
173±5.33
601±14.7
NA
413.7±14.9
E12.5
E13.5
E14.5
181.7±3.8
2.3±0.4
3.32.3±0.4
413.3±11.1
7.7±1.1
40±1.33
658.7±26.9
NA
87±7.33
Embryos from the same litter were used for comparison.
NA, not available.
DEVELOPMENT
Table 1. Determination of RGC numbers using anti-Pou4f2/Brn3b antibody with retinal sections from different genotypes and
developmental stage
bHLH genes and retinal ganglion cell fate
RESEARCH ARTICLE 3385
DISCUSSION
Our results demonstrate that Neurod1, and to a lesser extent Math3,
can replace Math5 to direct RPCs towards an RGC fate. Expressing
Neurod1 at the Math5 locus in the absence of Math5 resulted in a
substantial restoration of all aspects of RGC differentiation, including
axonogenesis and formation of the optic nerve. Neurod1 was better at
replacing Math5 than was Math3, but the presence of both factors
appeared to have an optimal effect. One possible explanation for this
is that Neurod1 and Math3 may have somewhat different DNAbinding propensities and thus activate distinct sets of Math5 target
genes. In the retinas of Neurod1 knock-in embryos, fewer cells
expressed genes that mark the onset of RGC differentiation; only 3040% of the number of wild-type RGCs was optimally observed in the
knock-in retinas. Although we did not perform a quantitative analysis
of RGC gene expression, both in situ hybridization and
immunostaining analyses indicated that on a per cell basis, gene
expression levels between knock-in and wild-type embryos were
comparable. This suggests that a threshold level of Neurod1 protein
is required to replace Math5 and that only a minority of RPCs
expressing the knock-in alleles achieve this threshold. Accordingly, it
might be expected that Math5Neurod1-KI/Neurod1-KI would have a stronger
restorative effect than Math5Neurod1-KI/lacZ-KI. However, we did not
observe significant differences between these two genotypes (data not
shown). In fact, Math5Neurod1-KI/Math3-KI resulted in slightly better
effects, suggesting that one copy of Neurod1 was sufficient to produce
optimal restoration. These data imply that the threshold level of
Neurod1 is modulated by other activity-limiting intrinsic factors, and
one copy of Neurod1 exhausts such factors.
Our study suggests that Neurod1 can be inserted into the RPC
program to re-establish RGC competence and assume the roles that
normally require Math5. However, the lack of over-production of
amacrine cells in Math5Neurod1-KI/Math3-KI retinas suggests that, in
addition to the ubiquitously expressed homeobox genes Pax6 and
Six3, other factors are required to act with Neurod1 and Math3 to
reprogram an RPC towards amacrine cell production. Precocious
formation of amacrine cells may have occurred in
Math5Neurod1–KI/Math3-KI retinas but may not have been tolerated in this
slightly earlier environment. In fact, we have detected significant cell
death within the central region of Math5Neurod1-KI/Math3-KI retinas as
early as E11.5, whereas no increase in cell death was seen in E11.5
Math5-null retina (Le et al., 2006), suggesting improper and
precocious cell differentiation may have taken place. The modest
increase in the number of cone cells in Math5Neurod1-KI/Math3-KI retina
indicates that a few Math5Neurod1-KI/Math3-KI-expressing cells assume a
cone cell fate – the next cell type to appear within the Math5expressing cell lineage (Cayoutte et al., 2006). The results presented
here support the view that an intrinsic program within the RPC
dictates the functions of Neurod1 and Math3, when they are expressed
at the Math5 locus. Therefore, the specialized functions that have
presumably evolved for these bHLH factors may not be as crucial in
determining early retinal cell fates as is currently thought.
The intrinsic program necessary for a naïve RPC to advance to a
specific competence state is thought to arise from a dynamic local
external environment. This environment changes through time and
continually provides instructions to RPCs to assume successive
competence states (Cayouette et al., 2006; Wallace, 2008). The
discovery of numerous transcriptional regulators essential for retinal
cell fate specification and differentiation has lead to the elucidation
of detailed genetic regulatory pathways that define the intrinsic
programs of most RPCs (reviewed by Ohsawa and Kageyama,
2008; Mu and Klein, 2008). However, the mechanisms connecting
the dynamic local environment within the developing retina to the
intrinsic genetic programs that operate in distinct RPCs remain
elusive.
Although we have emphasized the evidence that Neurod1 is more
capable of adapting to a foreign environment than Math3, our results
also show that neither Neurod1 nor Math3 can fully restore RGCs
in the absence of Math5. Neurod1 and Math3 together were slightly
more effective that Neurod1 alone, which in turn was more effective
than Math3 alone. Recently, it has been shown that a Math5Mash1-KI
DEVELOPMENT
Fig. 5. Neurod1 is more effective than Math3 in
activating Eomes in the absence of Math5.
(A1-D3) Retinas from E14.5 embryos were immunostained
with anti-Eomes (A1-D1) or anti-Pou4f2/Brn3b
(A2-D2) antibodies. Merged images are shown in A3-D3.
(A1-A3) Wild type, (B1-B3) Math5Neurod1-KI/Math3-KI,
(C1-C3) Math5Neurodi-KI/lacZ-KI and (D1-D3) Math5Math3-KI/lacZ-KI.
3386 RESEARCH ARTICLE
Development 135 (20)
allele has only modest restorative ability (Nadean Brown, personal
communication). The obvious explanation for these differences is
that all of these bHLH factors have amino acid sequence differences
within and outside of their bHLH domains that are likely to reflect
differences in protein-protein interactions, post-translational
modifications, and promoter-enhancer preferences. For example,
three amino acids within the basic domain of chicken Ath5 are
reported to be crucial for protein-protein interactions that confer
DNA binding specificity to Ath5 (Skowronska-Krawczyk et al.,
2005). These residues are found in Math5 but not in Neurod1, Math3
or Mash1 (see Table S3 in the supplementary material). The helix 1
and helix 2 domains within Mash1 and Math1 are crucial in
determining neuronal differentiation (Nakada et al., 2004). Thus, the
sequence differences in helix 1 and helix 2 domains among Math5,
Neurod1, Math3 or Mash1 might account for their differential
function in the same environment (see Table S3 in the
supplementary material).
In their normal cellular context, Math5, Neurod1 and Math3 are
likely to regulate the expression of distinct sets of target genes
during retinal development. Recent reports have identified
downstream target genes for Math5, but there is little information
on the overlap of these target genes with the genes regulated by
Neurod1 and Math3 (Mu et al., 2005; Del Bene et al., 2007).
Moreover, differences in cellular context could result in posttranslational modifications that affect DNA-binding site choice. In
Xenopus, a post-translational mechanism mediated by GSK-3β
phosphorylation negatively regulates the ability of NeuroD to
promote RGC differentiation (Moore et al., 2002). Although
mouse Neurod1 lacks the GSK-3β phosphorylation site, other
kinases such as ERK may modulate the function of Neurod1 when
it is expressed at the Math5 locus in Math5-expressing RPCs
(Dufton et al., 2005).
Several studies have reported on the effects of replacing one
related transcription factor with another in a developmental context.
In many cases, gene swapping demonstrates a large degree of
functional redundancy and indicates that the timing of expression is
perhaps more crucial than specialized functions that might have
evolved. For example, in the retina, the closely related POU domain
factors Pou4f1 and Pou4f2 appear to be interchangeable in their
ability to function as regulators of RGC differentiation if they are
expressed at the Pou4f2 locus (Pan et al., 2005). In mid-hindbrain
development, the lethal En1 mutant phenotype can be rescued by
replacing En1 with closely related En2 (Hanks et al., 1995).
However, two related bHLH genes, Mash1 and Ngn2, have been
shown to maintain their divergent functions in the specification of
neuronal subtype identity in the dorsal telencephalon and ventral
spinal cord (Parras et al., 2002). In skeletal muscle, the myogenic
bHLH regulatory factor myogenin can substitute for the closely
related Myf5 factor in promoting myogenesis, although less
efficiently (Wang and Jaenisch, 1997). The same swap leads to
complete rescue of the lethal Myf5 mutant rib phenotype (Wang et
al., 1996). Similarly, in the sensory nervous system of Drosophila,
the proneural bHLH factor Amos, a bHLH factor closely related to
Atonal, can substitute for Atonal in specifying R8 photoreceptor fate
(Maung and Jarman, 2007), whereas another bHLH factor, Sc,
cannot (Sun et al., 2000). By contrast, Amos cannot rescue the
chordotonal phenotype seen in Atonal mutants (Maung and Jarman,
2007), suggesting that the developmental context is critical for
DEVELOPMENT
Fig. 6. Neurod1 and Math3 activate the RGC gene regulatory network in the absence of Math5. Math5Neurod1-KI/Math3-KI retinas from E14.5
(or E13.5 for G1,G2) embryos were analyzed by in situ hybridization (purple) or immunostaining (green). (A1-H1) Wild-type retinas.
(A2-H2) Math5Neurod1-KI/Math3-KI retinas. In situ hybridization probes and antibodies for immunostaining representing genes downstream of Math5
are indicated in the lower left-hand corner in A1-H1.
distinct bHLH factors to exert their specific activity. Our current
study suggests that developmental time and the intrinsic properties
within distinct RPCs largely dictate the roles of bHLH factors in
specifying early retinal cell fate.
In the developing retina, bHLH factors and other transcriptional
regulators produce a highly complex combinatorial state that defines
each RPC subpopulation (Ohsawa and Kageyama, 2008; Mu and
Klein, 2008; Mu et al., 2008). Thus, each subpopulation is under the
control of a specific gene regulatory network composed of
hierarchical tiers of transcription factors connected to their cis
regulatory sites on target regulatory genes (Ben-Tabou de-Leon and
Davidson, 2007). Although each network is distinct, its underlying
framework is likely to be similar to that of other RPC subpopulation.
The crucial nodes in the different RPC networks are likely to be
represented by transcription factors of the same class. There is little
question that bHLH factors have evolved specialized features for each
lineage, but the fact that they are sometimes interchangeable reflects
the flexibility of RPC gene regulatory networks. A given network can
tolerate the replacement of one bHLH factor with another, provided
the other factor has the capability of fitting into the network at the
correct hierarchical level to receive the inputs and transmit the outputs
that are required for successful network operation.
We thank Jan Parker-Thornburg, Sandra Rivera and the Genetically Engineered
Mouse Facility at The University of Texas M. D. Anderson Cancer Center for
generating the knock-in mouse lines. We also acknowledge the assistant of
Jodie Polan for confocal microscopy and imaging, and Eric Meadows for qRTPCR analysis. We thank Satchidananda Panda for providing the antimelanopsin antibody and acknowledge the M. D. Anderson Cancer Center
DNA Analysis Facility for DNA sequencing. We are grateful for insightful
comments on the manuscript provided by Xiuqian Mu. The Genetically
Engineered Mouse Facility and DNA Analysis Facility are supported in part by a
National Cancer Institute Cancer Center Core Grant (CA016672). The work
was supported by grants to W.H.K. from the National Eye Institute (EY011930
and EY010608-139005) and from the Robert A. Welch Foundation (G-0010),
and by a grant from the E. Matilda Ziegler Foundation for the Blind to S.W.W.
Supplementary material
Supplementary material for this article is available at
http://dev.biologists.org/cgi/content/full/135/20/3379/DC1
References
Akagi, T., Inoue, T., Miyoshi, G., Bessho, Y., Takahashi, M., Lee, J. E.,
Guillemot, F. and Kageyama, R. (2004). Requirement of multiple basic helixloop-helix genes for retinal neuronal subtype specification. J. Biol. Chem. 279,
28492-28498.
Bäumer, N., Marquardt, T., Stoykova, A., Spieler, D., Treichel, D., AsheryPadan, R. and Gruss, P. (2003). Retinal pigmented epithelium determination
requires the redundant activities of Pax2 and Pax6. Development 130, 29032915.
Ben-Tabou de-Leon, S. and Davidson, E. H. (2007). Gene regulation: gene
control network in development. Annu. Rev. Biophys. Biomol. Struct. 36, 191212.
Brown, N. L., Kanekar, S., Vetter, M. L., Gemza, D. L. and Glaser, T. (1998).
Math5 encodes a basic helix-loop-helix transcription factor expressed during early
stages of retinal neurogenesis. Development 125, 4821-4833.
Brown, N. L., Patel, S., Brzezinski, J. and Glaser, T. (2001). Math5 is required for
retinal ganglion cell and optic nerve formation. Development 128, 2497-2508.
Cayouette, M., Poggi, L. and Harris, W. A. (2006). Lineage in the vertebrate
retina. Trends Neurosci. 29, 563-570.
Coombs, J., van der List, D., Wang, G. Y. and Chalupa, L. M. (2006).
Morphological properties of mouse retinal ganglion cells. Neuroscience 140, 123136.
Del Bene, F., Ettwiller, L., Skowronska-Krawczyk, D., Baier, H., Matter, J. M.,
Birney, E. and Wittbrodt, J. (2007). In vivo validation of a computationally
predicted conserved Ath5 target gene set. PLoS Genet. 3, 1661-1671.
Dufton, C., Marcora, E., Chae, J. H., McCullough, J., Eby, J., Hausburg, M.,
Stein, G. H., Khoo, S., Cobb, M. H. and Lee, J. E. (2005). Context-dependent
regulation of NeuroD activity and protein accumulation. Mol. Cell. Neurosci. 28,
727-736.
Elshatory, Y., Deng, M., Xie, X. and Gan, L. (2007). Expression of the LIMhomeodomain protein Isl1 in the developing and mature mouse retina. J. Comp.
Neurol. 503, 182-197.
RESEARCH ARTICLE 3387
Gan, L., Wang, S. W., Huang, Z. and Klein, W. H. (1999). POU domain factor Brn3b is essential for retinal ganglion cell differentiation and survival but not for initial
cell fate specification. Dev. Biol. 210, 469-480.
George, S. H., Gertsenstein, M., Vintersten, K., Korets-Smith, E., Murphy, J.,
Stevens, M. E., Haigh, J. J. and Nagy, A. (2007). Developmental and adult
phenotyping directly from mutant embryonic stem cells. Proc. Natl. Acad. Sci. USA
104, 4455-4460.
Hanks, M., Wurst, W., Anson-Cartwright, L., Auerbach, A. B. and Joyner, A. L.
(1995). Rescue of the En-1 mutant phenotype by replacement of En-1 with En-2.
Science 269, 679-682.
Hatakeyama, J. and Kageyama, R. (2004). Retinal cell fate determination and
bHLH factors. Semin. Cell. Dev. Biol. 15, 83-89.
Inoue, T., Hojo, M., Bessho, Y., Tano, Y., Lee, J. E. and Kageyama, R. (2002).
Math3 and NeuroD regulate amacrine cell fate specification in the retina.
Development 129, 831-842.
Kim, J., Lander, A. L., Lyons, K. M., Matzuk, M. M. and Calof, A. L. (2005).
GDF11 controls the timing of progenitor cell competence in developing retina.
Science 308, 1927-1930.
Lagutin, O., Zhu, C., Furuta, Y., Rowitch, D., McMahon, A. P. and Oliver, G.
(2001). Six3 promotes the formation of ectopic optic vesicle-like structures in
mouse embryos. Dev. Dyn. 221, 342-349.
Le, T. T., Wroblewski, E., Patel, S., Riesenberg, A. N. and Brown, N. L. (2006).
Math5 is required for both early retinal neuron differentiation and cell cycle
progression. Dev. Biol. 295, 764-778.
Ledent, V., Paquet, Q. and Vervoort, M. (2002). Phylogenetic analysis of the
human basic helix-loop-helix proteins. Genome Biol. 3, RESEARCH0030.
Lin, B., Wang, S. W. and Masland, R. H. (2004). Retinal ganglion cell type, size,
and spacing can be specified independent of homotypic dendritic contacts.
Neuron 43, 475-485.
Livesey, F. J. and Cepko, C. L. (2001). Vertebrate neural cell-fate determination:
lessons from the retina. Nat. Rev. Neurosci. 2, 109-118.
Mao, C. A., Kiyama, T., Pan, P., Furuta, Y., Hadjantonakis, A. K. and Klein, W.
H. (2008). Eomesodermin, a target gene of Pou4f2, is required for retinal ganglion
cell and optic nerve development in the mouse. Development 135, 271-280.
Maung, S. M. and Jarman, A. P. (2007). Functional distinctness of closely related
transcription factors: a comparison of the Atonal and Amos proneural factors.
Mech. Dev. 124, 647-656.
Moore, K. B., Schneider, M. L. and Vetter, M. L. (2002). Postranslational
mechanisms control the timing of bHLH function and regulate retinal cell fate.
Neuron 34, 183-195.
Moshiri, A., Gonzalez, E., Tagawa, K., Maeda, H., Wang, M., Frishman, L. J.
and Wang, S. W. (2008). Near complete loss of retinal ganglion cells in the
math5/brn3b double knockout elicits severe reductions of other cell types during
retinal development. Dev. Biol. 316, 214-227.
Mu, X. and Klein, W. H. (2004). A gene regulatory hierarchy for retinal ganglion cell
specification and differentiation. Semin. Cell Dev. Biol. 15, 15-23.
Mu, X. and Klein, W. H. (2008). Gene regulatory networks and the development of
retinal ganglion cells. In Eye, Retina, and Visual System of the Mouse (ed. L. M.
Chalupa and R. W. Williams), pp. 321-332. Cambridge, MA: MIT Press.
Mu, X., Beremand, P. D., Zhao, S., Pershad, R., Sun, H., Scarpa, A., Liang, S.,
Thomas, T. L. and Klein, W. H. (2004). Discrete gene sets depend on POU
domain transcription factor Brn3b/Brn-3.2/POU4f2 for their expression in the
mouse embryonic retina. Development 131, 1197-1210.
Mu, X., Fu, X., Sun, H., Beremand, P. D., Thomas, T. L. and Klein, W. H. (2005).
A gene network downstream of transcription factor Math5 regulates retinal
progenitor cell competence and ganglion cell fate. Dev. Biol. 280, 467-481.
Mu, X., Fu, X., Beremand, P. D., Thomas, T. L. and Klein, W. H. (2008). Gene
regulatory logic in retinal ganglion cell development: Isl1 defines a critical branch
distinct from but overlapping with Pou4f2. Proc. Natl. Acad. Sci. USA 105, 69426947.
Nakada, Y., Hunsaker, T. L., Henke, R. M. and Johnson, J. E. (2004). Distinct
domains within Mash1 and Math1 are required for function in neuronal
differentiation versus neuronal cell-type specification. Development 131, 13191330.
Ohsawa, R. and Kageyama, R. (2008). Regulation of retinal cell fate specification
by multiple transcription factors. Brain Res. 1192, 90-98.
Pan, L., Yang, Z., Feng, L. and Gan, L. (2005). Functional equivalence of Brn3 POUdomain transcription factors in mouse retinal neurogenesis. Development 132,
703-712.
Pan, L., Deng, M., Xie, X. and Gan, L. (2008). ISL1 and BRN3B co-regulate the
differentiation of murine retinal ganglion cells. Development 135, 1981-1990.
Parras, C. M., Schuurmans, C., Scardigli, R., Kim, J., Anderson, D. J. and
Guillemot, F. (2002). Divergent functions of the proneural genes Mash1 and
Ngn2 in the specification of neuronal subtype identity. Genes Dev. 16, 324-338.
Rachel, R. A., Dolen, G., Hayes, N. L., Lu, A., Erskine, L., Nowakowski, R. S.
and Mason, C. A. (2002). Spatiotemporal features of early neurogenesis differ in
wild-type and albino mouse retina. J. Neurosci. 22, 4249-4263.
Skowronska-Krawczyk, D., Matter-Sadzinski, L., Ballivet, M. and Matter, J. M.
(2005). The basic domain of ATH5 mediates neuron-specific promoter activity
during retina development. Mol. Cell. Biol. 25, 10029-10039.
DEVELOPMENT
bHLH genes and retinal ganglion cell fate
Sun, W., Li, N. and He, S. G. (2002). Large-scale morphological survey of mouse
retinal ganglion cells. J. Comp. Neurol. 451, 115-126.
Sun, Y., Jan, L. Y. and Jan, Y. N. (2000). Ectopic scute induces Drosophila
ommatidia development without R8 founder photoreceptors, Proc. Natl. Acad. Sci.
USA 97, 6815-6819.
Tokuoka, H., Yoshida, T., Matsuda, N. and Mishina, M. (2002). Regulation by
glycogen synthase kinase-3beta of the arborization field and maturation of
retinotectal projection in zebrafish. J. Neurosci. 22, 10324-10332.
Tomita, K., Moriyoshi, K., Nakanishi, S., Guillemot, F. and Kageyama, R.
(2000). Mammalian achaete-scute and atonal homologs regulate neuronal versus
glial fate determination in the central nervous system. EMBO J. 19, 5460-5472.
Trimarchi, J. M., Stadler, M. B. and Cepko, C. L. (2008). Individual retinal
progenitor cells display extensive heterogeneity of gene expression. PLoS ONE 3,
e1588.
Vetter, M. L. and Brown, N. L. (2001). The role of basic helix-loop-helix genes in
vertebrate retinogenesis. Semin. Cell Dev. Biol. 12, 491-498.
Development 135 (20)
Wallace, V. A. (2008). Proliferative and cell fate effects of Hedgehog signaling in the
vertebrate retina. Brain Res. 1192, 61-75.
Wang, J. C. and Harris, W. A. (2005). The role of combinational coding by
homeodomain and bHLH transcription factors in retinal cell fate specification. Dev.
Biol. 285, 101-115.
Wang, S. W., Kim, B. S., Ding, K., Wang, H., Sun, D., Johnson, R. L., Klein, W.
H. and Gan, L. (2001). Requirement for math5 in the development of retinal
ganglion cells. Genes Dev. 15, 24-29.
Wang, Y. and Jaenisch, R. (1997). Myogenin can substitute for Myf5 in
promoting myogenesis but less efficiently. Development 124, 2507-2513.
Wang, Y., Schnegelsberg, P. N., Dausman, J. and Jaenisch, R. (1996).
Functional redundancy of the muscle-specific transcription factors Myf5 and
myogenin. Nature 379, 823-825.
Yang, Z., Ding, K., Pan, L., Deng, M. and Gan, L. (2003). Math5 determines
the competence state of retinal ganglion cell progenitors. Dev. Biol. 264, 240254.
DEVELOPMENT
3388 RESEARCH ARTICLE